Accepted for/Published in: Online Journal of Public Health Informatics
Date Submitted: Apr 16, 2026
Date Accepted: Aug 4, 2026
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Enhancing Situational Awareness to Combat Health Misinformation on Social Media: A Health Belief Model Approach
ABSTRACT
Background:
The rapid expansion of social media during the COVID-19 pandemic accelerated the spread of health misinformation, contributing to public confusion, mistrust in health authorities, and reduced vaccine uptake.
Objective:
This study aimed to develop a situational awareness framework to mitigate health misinformation on social media within a developing country context.
Methods:
Adopting a positivist quantitative approach, data were collected from 214 social media users aged 18–35 using a non-probability convenience sample, and analysed through Structural Equation Modelling using SPSS and SmartPLS.
Results:
The findings indicate that perceived susceptibility, perceived severity, health motivation, perceived benefits, and cues to action significantly influence users’ intentions to counter misinformation, while self-efficacy negatively affects engagement and perceived barriers show no significant impact.
Conclusions:
Based on these insights, a situational awareness framework is proposed to support users in identifying and managing misinformation, offering practical implications for digital communication strategies and public health interventions.
Citation
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Copyright
© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.